Effects of drag force correlations on the mixing and segregation of polydisperse gas‐solid fluidized bed by CFD‐DEM simulation
Bibliographic record
Abstract
Abstract The dense gas‐solid fluidized beds with polydisperse particles are commonly encountered in the chemical industry. In this study CFD‐DEM simulation of ternary particles in a bubbling fluidized bed were studied to assess four polydisperse drag correlations, using experimental data from Olaofe et al.[1] as the benchmark. The following was shown: (i) the drag force models of Sarkar et al.,[2] Cello et al.,[3] and Rong et al.[4] can correctly predict the minimum fluidization velocity of the mixture of ternary particles, but Gidaspow's model significantly over‐predicts this;[5] (ii) no model can accurately predict the experimental segregation degree at different gas velocity, but for the cases that were studied, the models from Cello et al.[3] and Sarkar et al.[2] correlated relatively better with Olaofe et al.'s[1] experimental data than others; and (iii) detailed force analysis found that the collisional force of each type of particles is on average not equal to zero, especially for a system that is strongly segregated. In addition, the differences of drag force for different sizes of particles are the key reason for size segregation rather than pressure gradient force.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".